ENGL 87400 / Graduate Center, CUNY / Spring 2015

Inspired by Ashleigh’s presentation, I want to share an excellent article from the archival theory journal Archivaria, “Records of Simple Truth and Precision: Photography, Archives, and the Illusion of Control,” which argues that the advent of the daguerreotype in 1830s France had epistemological parallels in 19th c. French archival theory, which privileged the archival fonds as an objective and organically produced historical artifact.

To try out the Ivanhoe game,I’ve created a new WordPress install on my site and activated the Ivanhoe theme. If you want to try it out, head to

ivanhoe.pbsmyth.com

You can log in at:

ivanhoe.pbsmyth.com/wp-admin

Use the username “admin” and password “texttransformations”

My first impression is that this might be a good game to run in a lit class. I titled the site “Handmaid’s Tale,” as Atwood’s classic seemed like a good candidate for roleplay. Feel free to create new games or users on the site to try things out. Looking forward to our discussion tomorrow!

Here’s the project that I mentioned in class, created by Molly Rosner who is in the American Studies PhD program at Rutgers-Newark: https://bkinloveandwar.wordpress.com/

About this Blog

This blog looks at the nation’s history as filtered through the well-documented relationship between my grandparents. I never knew Sylvia, but she and my grandfather, Alex, wrote hundreds of letters to each other during the years that Alex was stationed abroad during WWII.

Each post examines a letter that helps the story of two people unfold as they navigate their relationship with each other and with the war.

I will also look at the letters in the context of Brooklyn and how it has changed over time. The blog is also a platform to examine different methods of storytelling that are used throughout the letters.

With great enthusiasm for the parade of bots we’ve unleashed upon Twitter, I’m writing to introduce you to @OldEnglishBot, my Old English-in-translation utterance generator, and to share the process of its creation.

As you’ve probably gathered from my signature catchphrase, “as a medievalist…” (sorry about that), I have a particular interest in the early stuff: especially Old English verse and prose. This, of course, necessitated learning Old English, which isn’t known as a riotously fun process and also has waning institutional support (not at CUNY Grad Center, though!). This is unfortunate, because there is a rich trove of Old English poetry: much of which has influenced twentieth-century poetics, and also deserves new or experimental translation—check out the Old English Poetry Project for examples of the latter. I’ve been thinking a lot lately about how to make Old English literature accessible and interesting to non-medievalist scholars, so we can buck the stereotype that early medieval study is a dusty old activity that exists in isolation from English literature at large.

Enter @OldEnglishBot, my Twitter experiment. I wanted to produce a bot that could create a conversation about Old English for more than just early medieval scholars. So instead of producing tweets in Old English—which sort of defeats the purpose of Twitter’s skimming format, since you’d have to slow down and translate them, and that would render the bot totally uninteresting to most of the population, academic or otherwise—I decided to make this an experiment in translation, too. My goal was to take a corpus of Old English words in translation, and then generate random utterances from this data set. These utterances, I feel, might offer a creative way of envisioning the type of themes, sentiments, and general moods that Old English can express—serving as a creative deformance, after Jerome McGann and Lisa Samuels in “Deformance and Interpretation,” to perhaps draw scholars, and even poets, into this evocative world of study.

I used a list of words generated by Professor Christine Rauer, of University of Saint Andrews, that she describes as a core vocabulary for Old English. According to her introduction to the list, the words are selected based on their frequency across the Old English corpus, or their “importance on account of their literary or linguistic usage.” The list is available here: http://www.st-andrews.ac.uk/~cr30/vocabulary/. Her list was ideal as a starting point for this bot project, since it contained approximately 500 words, and was also categorized according to part of speech—which came in handy later.

While future iterations of @OldEnglishBot might be best served by a complete Old English corpus, which does not yet exist online, I might see if further research yields a digitized copy of either the Clark-Hall or Bosworth-Toller dictionaries, which could yield a fuller picture of Old English words.

I think the idea of Twitter bots is very pedagogically inspiring, and wanted to experiment with ways to create bots that I could easily teach to others. So, I searched for a protocol that could produce a bot quickly so I could get to the good stuff—looking at tweets, theorizing their results, and thinking critically about play and deformance in algorithmic settings. Professor Zac Whalen, at the University of Mary Washington, has produced an ideal tool for this: a Twitter bot that can be produced in about half an hour, minus data preparation, based on a Google Spreadsheet. You can read more about his painfully simple instructions here: http://www.zachwhalen.net/posts/using-google-spreadsheets-for-a-generated-text-twitter-bot, and about his own thoughts on Twitter bots, poetics, and Markov chains here: http://www.zachwhalen.net/posts/twitter-bots-markov-chains-and-large-slices-of-clarity.

Most of the time spent in this Twitter bot exercise was spent formatting my data. I used an Excel spreadsheet to separate strings into individual columns, which I then sorted by part of speech. For words that had two or more meanings or connotations, I created an additional entry for each meaning so that my columns each had one word, and I regularized the verbs from infinitive form into third person present singular tense in modern English. Since Whalen’s protocol randomly generates utterances, I anticipate the verbs to cause some problems at certain points because not all my nouns are singular, but decided to give it an imperfect shot for my first try. Also, I elected not to use punctuation, since Old English manuscripts do not use it—I thought it might be useful and intriguing to let meanings proliferate by keeping orthography to a minimum.

Ultimately, this database component was a great reminder of how our language learning processes a variety of rather complex compositional rules, and the capabilities or limitations of replicating this process in digital environments—and it would be a great pedagogical activity, too. If I were to teach Twitter bots, especially in an undergraduate course or to scholars that are not computer experts (like me), I would discuss bots and their theoretical interest, assign the conception of a bot and the creation of a corpus database in preparation for the next class, and then use Professor Whalen’s super-simple tool to make the bots in class (it really is that easy). I would have students set them on two or three minute timers so we could discuss our bot creations, troubleshoot, and consider what questions bots raise for humanities study in real time!

I’m still watching the results unfold, and tweaking with my corpus in order to produce the most consistent results, but I’ve had some great Tweets out of the gate: “The lord produces consolation dear and far,” “The counsellor repents lord cold and eager for praise,” and “The keeper instructs deer unknown and manifest,” all have a definitively Anglo-Saxon feel, and give a good sense of the types of utterances that do in fact occur in Old English. There’s also an interesting poetic vibe to these utterances, and I’d love to get more poets involved to do surrealist poetry exercises with these tweets. One such poet on my list is Conley Lowrance, bot-creator of @poet_noir, who writes poetry based on surrealist techniques and the element of the uncanny and chance in detective fiction. He is also my husband, which is of course another story, and created his bot after I told him about our class this past Monday—vive la revolution! I know other poets across Twitter are interested in digital composition too, and especially given the troubled boundary between “creative” and “critical” modes of writing that we discussed in class, I think poets have much to add to bot theory and its possibilities.

To conclude for now: I suppose the goal of @OldEnglishBot is to lure unsuspecting poets and scholars in with the atmospheric tone of Old English literature and the pleasurable algorithmic serendipity of bots, so that they will immediately sign up for language courses, start doing radical and exciting translations, and foster a creative and pedagogical community for this literature that I so love. Perhaps this is a lofty goal for a little bot, especially one so buggy as mine, but I certainly think it’s a possibility of algorithmic and deformative play. We will see how it goes as the tweets continue.

I took a different approach to this exercise, having not really made connections to our readings about text deformance until reading the other blog posts before class. My bots (or hypothetical bots) were conceived as research tools rather than text generators.

First: wanted to set up an account that tweeted a random audio clip from my oral history site (www.seamenschurch-archives.org/sci-ammv). I would need the bot to pull both the clip title and the clip description from the metadata, ideally. I quickly realized this is very difficult to set up for someone with very little exposure to programming.

Next: I decided to use a ready-made service, RoundTeam, to set up a simple keyword retweet that I could use as a research tool. This is generally how I use Twitter anyway–as a content aggregator in which I follow people who post content or links to content, and I re-tweet things that I’ve read or interacted with. It’s a sort of digital reading journal for me. From an archival perspective, this is what I find most interesting about Twitter: that users are leaving digital marginalia that represent traces of their interactions with the “text” of the internet. The Library of Congress Twitter archive project seems promising, but I wish there was more transparency as to what exactly they are doing.

Anyway, since I research the cultural history of urban port districts (aka “sailortowns”) I created an account “SailortownRTs,” and attempted to configure it so that it would retweet any mention of the keyword “sailortown,” but immediately ran into the problem that applying this function to all of Twitter requires a paid account.

Admittedly, I haven’t spent much time attempting to figure out how to do this from scratch. I plan on seeing what I can do over the weekend drawing on other examples.

My week has been a veritable twitter roller coaster, and I owe an ENORMOUS debt of gratitude to Patrick, without whom I might still be combing my way through StackOverflow error solutions. Actually, I might not have even gotten to those without help, but might still be plugging away at my Code Academy python basics.

I have had a very curious experience of the building phenomenon. People respond to the idea of a twitterbot in many different ways — more tech-savvy (snarky) friends “you’ve gone to grad school to become a spammer?” to the more tech-indifferent (kindly indulgent) “wow. you’re from the future!” (I do tell them I’m at least two years behind the trend). Many people had no idea what I was talking about (not just my mother, other people, too).

My immediate favorite, Stealth Mountain, I still aim to emulate. Though I am no wikignome crusader, I do love grammar rules and puns…. but I decided to aim for a simple random choice function. Jokingly, while leaving class last week, I told Matt I’d be making bots like I make commons wordpress sites (don’t look now), and then I said I’d make one as a birthday present for my roommate (who is working on her first Middle Grade fantasy novel). I asked her what she might want, and when I suggested something that concocted descriptions from words, she said she would like plots of Y.A. novels she could write when she finishes hers. So I set to work.

I set up my Twitter developer stuff and got my key and secret (so cloak and dagger!). Then I spent a little too long just figuring out where to write my script. (Vims and TextWranglers and Emacs, oh my!).

Friday, I sat down in the Digital Fellows office and Patrick talked me through the tricky parts. I still have some syntax issues to sort (in the sentence), but the code seems to be working. And it has 41 followers — only half of which are other bots!

MY BOT’s FIRST TWEET!

MY BOT’s growth spurt (& slightly naughty phase).

I have taken down the pictures of Greg the Dog and replaced them with something at least remotely connected to my random assortment of words. I did consult with a children’s librarian (my sister) to get some more typical words to throw into the mix (until the time comes that I can successfully scrape these words from somewhere else). And I’ve even gotten a request to illustrate the plots (Thanks, @kellyblanchat, for your artistic support!).

When I have slept some, I will try to update this post with more explicit information about the steps I took (I have so many screen shots), but for now I need sleep and a chance to get some reading done.

When I started searching around for information on Twitter Bots, the same page kept coming up: Automation Rules and Best Practices. Here, Twitter outlines their philosophy on the automatic triggering of certain events across their site.

Just wanted to post a link to my Twitterbot, Technocracy News Today. The idea is to combine random buzzwords from tech and venture capital to create hyperbolic news stories. My code is on GitHub. It’s pretty simple…I built it in Python and used the Tweepy library to connect to the Twitter API. The gen.py script creates a text file full of randomly generated tweets. The tbot.py file takes the tweets.txt file and uses it to tweet every 90 minutes or so.